On this week's Industrial Talk we're onsite at IoT Solutions World Congress and talking to Dr. Michael Grieves, Executive Director and Chief Scientist, Digital Twin Institute about "Digital Twin and where it's headed". Learn from this Digital Twin pioneer along with and Dr. Grieves's unique insight into the future of Digital Twin on this Industrial Talk interview! Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2023. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy!

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PODCAST TRANSCRIPT:

SUMMARY KEYWORDS world, digital, twin, data, problem, physical, industrial, digital representation, information, changing, maintenance, year, iot solutions, iot, asset, motor, congress, industry, virtual, computing 00:03 Welcome to the industrial talk podcast with Scott Mackenzie. Scott is a passionate industry professional dedicated to transferring cutting edge industry focused innovations and trends while highlighting the men and women who keep the world moving. So put on your hard hat, grab your work boots, and let's go 00:21 Alright, once again, we're broadcasting live from the IoT Solutions World Congress here in Barcelona, Spain, it is 2023. And I want to make sure that you put this on your calendar for next year. If you're not here. You need to be here. Because there are a lot of great professionals looking to solve problems and collaborate with you. And you need to be in this digital transformation journey. You just must be. So come here, IoT Solutions World Congress, of course, it's a Barcelona that never disappoints. All right, you're listening to industrial talk, you know about it. It is a platform dedicated to industrial professionals all around the world because you're bold, brave and daring greatly. You're changing lives. You're solving problems, and therefore, we celebrate you on this particular podcast. So always sign up, be ready. All right, in a hot seat. Michael Rivas Grieves two thing right off the bat screwed it up. But it doesn't matter. Because he came up with the concept of digital twin right? I did there it is. Grieves in the hotseat? Let's get cracking. Yeah. Well, how often have you been here? 01:29 So this is my first time in Barcelona, although I did do a session for the IoT, when it was COVID Time virtually so. 01:38 Okay. It's sort of odd, you've got this whole digital twin background? How long has it been around? How old it wouldn't it? Okay. I'll just go. We're not going into like, give us a little background? Because, no, we're not going to do that. But we are going to go into the fact that like, what was the genesis? What were you just sort of sitting around? And then all of a sudden? Wouldn't it be great to have a digital representation of? 02:04 Well, so so. So I've been in the computer industry since I was 16 years old. I mean, so we're talking is 45 right now, just yeah, I'm over 70. So So I mean, we had mainframes that, that your watch would perform at the moment. But but I'd always had the idea that the information you could pay instantly have information look different than the physical thing. So so if I have your water Bible, and I want to know the measurements, I gotta basically I got to have that water bottle. But when I have a digital twin, I can basically just have the information about it. Okay, so so you could strip the information on things and have it as a separate thing. And so I had a fairly large computer company, in the back start in the 80s. And I took the public in the 90s. And then retired because I was tired of playing with the lawyers and 02:53 accountants. It's a grind, it's a grinder, that's a whole nother conversation we've had I understand the pain. 03:00 Yes. So So I retired, I failed miserably retirement. And I went back and had gotten my doctrine and had thought about this, well, this is a cool idea. And so, so I graduated, and God was at the University of Michigan. And I came up and said, Well, here's the model, here, you have a physical thing, you have a virtual thing. And then you connect it by taking data from the virtual world, or the physical world, to populate the virtual world. And then you can use that information because my core premises information is a replacement for wasted resources. For any task that I have. I can divide it into two pieces if I was a missionary and omnipotent. And if you ask my wife, she'll tell you, I'm not either. But I know that two years, you 03:42 probably have out, you're just, you're just chirping my language. 03:46 Yeah. So So if so if I if I, what I want to do is use so so most of life is, is basically goal oriented, minimizing resources, right? And that's what we want humans to do. Yep, everything above that is wasted, okay. I can take information, I can't replace the physical resources, I need to actually do the minimal amount of that. But it can be a replacement for all the wasted resources. So So the examples I use, give me an example. So if I'm designing a product, and it's never gonna work to you, it'd be nice to know that before I spent all that money or I'm manufacturing something that's out all I'm trying to get three kids to practices, and they've changed the practice location, and they cancel one of the practices, right. So so those kinds of tasks, I can use the information to replace wasted resources. So that's why I want to do that. That's why I wanted to basically create this digital twin that has information so I can say, Hey, stupid, don't do that because they didn't have good work. 04:45 But did you have at that time, you're saying okay, I got it. Here's, here's, here's, here's the physical let's say that asset, physical asset, I have to place devices on that I have to I have to get data from that physical right to go over here, right? It's that point of data, then being able to put it here. Yes. Was that? I mean, did that exist? 05:09 Well, so, so, interesting questions. So So in theory, so so so at a minimum, I've got humans who could say, Okay, that's it. Exactly. And that's sort of what we've done since time immemorial. How many things are in the warehouse Joe? And so, so, so, initially, IoT didn't exist. But but we did have sensors, and we we've had, we feel sees you had, yeah, we didn't have. So that's a lot. So I figured we can get the data in some fashion. Now, IoT has enabled this dramatically, by having sensors and communications. And too often, I don't think we talked about the digital twin, the physical twin is pretty damn important because the physical world, Trump's the virtual world every day, so so we've got to get that information out. And we've got to send it over to the virtual world, so that I can then use that information to not waste resources in the physical world. And and you there 06:05 has to be this level of confidence, you can't just send garbage from the physical to the digital, and then say, Absolutely, oh, my gosh, somebody's gonna get into trouble. And it's not going to be pretty 06:15 Yeah, so so. So the validity of the data and the fact that the virtual world needs to, to basically reflect the physical world in real time, right? So you're gonna tell me you're designing a digital carpet in your, in your virtual world, a flying carpet, you know, ain't gonna fly because you can't, can't do that. So you're right, that, you know, the, the accuracy of reflecting the physical world. So this is not a video game where you can do all kinds of things. Yeah, it actually needs to reflect the, the the environment of the of the virtual world has to reflect the physical world, it's got to have great fidelity of that. 06:53 Yes, and, and what makes it so spectacular is the simple fact that once you, you crack that egg, once you have a high level of confidence, in that, that virtual representation of the physical world, then your ability to simulate, play around over here, tweak, do whatever is necessary to optimize whatever that digital world looks like, then to achieve and put it in a physical. So you achieve that over here with confidence. 07:26 And what the great thing is, is you talked about time machines, I got a time machine now, yeah, I don't have to have the same time, I can run backward in time, I can run forward in time. And so we're I view this as going is that the virtual world is a crystal ball into the future. So at every time zero, I'm going to collect the information from what's happening in the physical world, send it over and run a simulation to say what's going to happen, and it's going to come back and say, Okay, there's a 80% chance that you're gonna have a crash, because this is gonna break on you, okay? And it's gonna happen in four weeks, there's a 90% chance that it's going to happen in six weeks. So it's gonna give you percentages of things that are going to happen. And then you if you're not an idiot are going to pay attention to that. And, and not do that. So. So a lot of say, industry 4.0 was about, I'm going to start, I'm going to decrease the time between having a problem and having remediate that problem. My perspective is, I don't want to have a problem, I want to predict it's going to happen. And when I see that, go fix it beforehand. 08:25 And you're you're, you're talking that analytic capabilities that increase probability to that increase competence of saying, Okay, here's this data, run it. Okay, now, I'm going to change the parameters, run it, and then I'm going to continue to hone that so that it makes sense. And are we talking about this, this simulation capabilities within your digital representation, that it's going to continue to sort of learn a little bit more, get a little bit more advanced, and then just by virtue of the real ruler, reaps the benefit of that optimization, 09:02 so one of my digital twin types is what I call a digital twin aggregate. So if I get to, the bigger the population, I get stuff that happened, the better data that I have about when this when I see this sensor reading and this sensor, reading this sensor reading, I'm gonna have this problem. I now I keep honing that information, that data to basically create better and better models, if you will, of what's going to happen when I see those sorts of things. 09:28 It's exciting. Don't get me wrong, I get all geeky about the whole thing. Let me ask this question. So let's say I am I've got my digital representation of this Moto, Moto X, Y, Z, right? It's a common Moto X, Y, Z. It is in this environment. The the environment is a moderate environment, whatever it's, you've got information about motor XYZ in this environment. Is there value of that data? Because at mycompany over here has Moto X yc. And I want to do the same thing over there, or you want to get mad at us all. 10:08 And so so there's sort of two pieces to this, okay, is I need a digital twin of the motor, and I need a digital twin of the environment behavior. There you go. And so so I went both of those. And again, the more data I collect, okay, the better I can generate information. So remember, my information is replacement for wasting resources. So, so that's where my value is, is being able to process this data, and be able to then draw, so So I really like causation. But I'll settle for correlation. 10:40 But But again, let's say now, now, all of a sudden, you're, you're collecting all this valuable information on that asset, that motor? And I know that that motor is a common motor that is used in other processes around the world, whatever. Do you have? Do you have some sort of, can you I hate to say, sell the intellectual to sell that information to say, Hey, we got the solution to make that motor run efficiency. Do it this way? Here you go. So 11:09 so when I talked about it, the panel discussion today, as I said, look at there's two uses of this, of this digital twin. One is within your organization to to be more effective and efficient. Yep. The second is a new revenue stream. So there it is. So So I so I can monetize that. And it's happening today. I mean, for example, let's take a jet engine manufacturer, okay, an airline only has certain amount of jet engines, right? The jet engine manufacturer could have the data from all those engines and say to the airline, you want better you want better data about, you know, keeping those engines up. I'll tell you 11:47 what it make sense. Because there's, there's a, there's a benefit of that, right after talking about you value benefits. Let's just say safety in general. I agree. And, and of course, the revenue associated with it is that you're ensuring based off of data, that that asset stays productive, as much as it possibly can. And then you can perform the maintenance of whatever it is, is like, Hey, I see the data is doing this, you know, this weather, whatever, whatever the points are, boom, I'm going to do some maintenance on it at the optimal time. 12:20 Absolutely. The optimal time is when it's going to fail. And so again, this is my, my crystal ball, I'm looking at it and saying, This is gonna fail in two weeks, I'm gonna fix it tomorrow. 12:30 That's right. And it, and I've thought through this, too. So if I'm the treatment of maintenance dollars, right, I have maintenance, I budget that maintenance, the maintenance from a financial treatment of that is one for one minus up here, minus down to the bottom, right, it's different than capital you're gonna do. And there's there are companies that want to sweat the asset, let you know, do not do with a maintenance. But if you had that crystal ball, you sweat the asset, and you perform maintenance right at that optimal time. That's significant value. 13:06 Here, there's huge, huge, you know, we do periodic maintenance. So some stuff gets fixed before it breaks, and some stuff kids after, you know, and so, so if I could only fix it when it was gonna break. I'd save a huge amount of 13:20 I don't even know what the value is on that. But it is it's massive. 13:23 Well, let me tell you, when it talks, you're talking about safety is priceless. I mean, if I could save human lives, what value we do put value on that, but but from my perspective, is I could save somebody's life on that. I want to do it. And it 13:36 does, it doesn't even address the fact that when when you let's say you do planned maintenance, right, now it's time to do this motor, now it's time to do this motor and so on. You you run the risk of introducing flaws in your action right there. So I don't know. And you know, it's gonna get there it is, it's gonna get there. 13:57 And then we're having so much computing coming online. We're gonna we're, we, you know, I predict that, you know, we just passed 80 billion transistors on a chip or equivalent by 2030. It's about 6 trillion. So, 14:11 so Oh, don't gloss over that data. What did you just say? 14:14 So, so if you're familiar with Moore's law, we started off with 2k. In the 70s. We just passed 80 billion transistors on the chip. And this last year, my projections is if I follow that curve by 2030, I have 6 trillion transistors on a chip 128 times increase by 2040 about 100 and 120 100 30 trillion transmission on chip we're having huge amounts of computing coming online. 14:42 Hope my my, my head kid wrap around 14:46 that nobody's cancer. By the way, if we get to quantum computing, or perhaps 14:52 that's a whole nother conversation and it's gonna get it's gonna happen. Come on, talk to me. Tell me so. digital twin guy 15:00 So so I think that there will be specific problems quantum computing, general purpose quantum computing is really tough. And there's some theories that says you can get there from here. And that we're not real anyway, if we do so. Sorry. 15:18 Because everybody, there's, there's, there's some major cabbage being invested in quantum computing, major cabbage. 15:24 So I'd say I think for some special purpose problems, you will be able to use qubits to do that. But general purpose, you know, the computers we have today. I don't know unnecessarily now, the error correction problem is a big problem. 15:40 There you go. Well, you are being...